Pittsburgh Response to Endovascular therapy (PRE) score: optimizing patient selection for endovascular therapy for large vessel occlusion strokes
Bibliographic record
Abstract
BACKGROUND: Endovascular therapy seems to benefit a subset of patients with large vessel occlusion strokes. We aimed to develop a clinically useful tool to identify patients who are likely to benefit from endovascular therapy. METHODS: In a derivation cohort of consecutively treated patients with anterior circulation large vessel occlusion (Grady Memorial Hospital, N=247), independent predictors (p<0.1) of good outcome (90-day modified Rankin scale score (mRS) 0-2) were determined using logistic regression to derive the Pittsburgh Response to Endovascular therapy (PRE) score as a predictor of good outcome. The PRE score was validated in two institutional cohorts (University of Pittsburgh Medical Center (UPMC): N=393; Unitat d'Ictus Vall d'Hebron: N=204) and its discriminative power for good outcome was compared with other validated tools. Benefit of successful recanalization was assessed in PRE score groups. RESULTS: Independent predictors of good outcome in the derivation cohort (age, baseline National Institute of Health Stroke Scale (NIHSS) score and Alberta Stroke Program Early CT Score (ASPECTS)) were used in the model: PRE score=age (years)+2×NIHSS-10 × ASPECTS. PRE score was highly predictive of good outcome in the derivation cohort (area under the curve (AUC)=0.79) and validation cohorts (UPMC: AUC=0.79; UIVH: AUC=0.72) with comparable rates of good outcome in all PRE risk quartiles. PRE was superior to Totaled Health Risks In Vascular Events (THRIVE) (p=0.03) and Stroke Prognostication using Age and NIHSS (SPAN) (p=0.007), with a trend towards superiority to Houston Intra-Arterial Therapy 2 (HIAT2) (p=0.06) and iSCORE (p=0.051) in predicting good outcomes. Better outcomes were associated with successful recanalization in patients with PRE scores -24 to +49 but not in patients with PRE scores <-24 or ≥ 50. CONCLUSIONS: The PRE score is a validated tool that predicts outcomes and may facilitate patient selection for endovascular therapy in anterior circulation large vessel occlusions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".